Run mesocircuit

Main example script to run a simulation of the mesocircuit with NEST and subsequently analyze and plot the results. The simulation of the spiking neuronal network can be followed by an LFP simulation and the corresponding postprocessing and plotting.

The script mesocircuit_framework contains the main functionality for parameter evalutation and job execution. The mesocircuit framework allows for the evaluation of parameter spaces, but here we only simulate one individual parameter combination. Several interesting parameter combinations (overwriting default values) are collected in a dictionary in the script parametersets for convenience.

from mesocircuit import mesocircuit_framework as mesoframe
import parametersets

Here, we choose the parameter set mesocircuit_MAMV1 which is the default model. It is an upscaled version of the microcircuit representing area V1 of the Multi-Area Model (Schmidt and van Albada, 2018). ‘mesocircuit_MAMV1_evoked` applies a thalamic stimulus to the center of the same model. For local testing, local_microcircuit_PD and local_mesocircuit_PD are good choices. These models base on the original microcircuit (Potjans and Diesmann, 2014) and are downscaled for execution on a laptop.

name = 'mesocircuit_MAMV1'
# name = 'local_mesocircuit_PD'
params_key = name
custom_params = parametersets.ps_dicts[params_key]

Next, we instantiate a MesocircuitExperiment with the custom parameters. The argument name can be chosen freely; here we just use the name of the parameter set. Upon instantiation, data directories are created, derived parameters calculated, and job scripts written. If an already existing experiment should be loaded, a class can be instantiated with the arguments of the existing name and load=True.

meso_exp = mesoframe.MesocircuitExperiment(name, custom_params)

A MesocircuitExperiment provides an overview over all the parameter combinations it is holding (parameterview) and a list of all the individual model instances of class Mesocircuit` (circuits).

print(meso_exp.parameterview)
print(meso_exp.circuits)

For each Mesocircuit jobs can finally be launched. All provided jobs are run one after the other. analysis_and_plotting is for convenience combined into one job, but analysis and plotting can also be handled as individual jobs. For running the full model on an HPC cluster, machine=’hpc’ is required for submitting batch scripts via slurm; for a local test run machine=’local’ should be selected.

circuit = meso_exp.circuits[0]
circuit.run_jobs(
    jobs=[
        'network',
        'analysis_and_plotting',
        # 'lfp_simulation',
        # 'lfp_postprocess',
        # 'lfp_plotting',
    ],
    machine='hpc',
    # machine='local'
)